quickstart_demo_spacy.ipynb

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    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "/opt/conda/envs/glirel/lib/python3.10/site-packages/transformers/convert_slow_tokenizer.py:560: UserWarning: The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option which is not implemented in the fast tokenizers. In practice this means that the fast version of the tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these unknown tokens into a sequence of byte tokens matching the original piece of text.\n",
      "  warnings.warn(\n",
      "/opt/conda/envs/glirel/lib/python3.10/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of relations: 5\n",
      "\n",
      "Descending Order by Score:\n",
      "['Apple', 'Inc.'] --> headquartered in --> ['California'] | score: 0.9854260683059692\n",
      "['Apple', 'Inc.'] --> headquartered in --> ['Cupertino'] | score: 0.9569844603538513\n",
      "['Steve', 'Wozniak'] --> co-founder --> ['Apple', 'Inc.'] | score: 0.09025496244430542\n",
      "['Steve', 'Jobs'] --> co-founder --> ['Apple', 'Inc.'] | score: 0.08805803954601288\n",
      "['Ronald', 'Wayne'] --> co-founder --> ['Apple', 'Inc.'] | score: 0.07996643334627151\n"
     ]
    }
   ],
   "source": [
    "import spacy\n",
    "import glirel\n",
    "\n",
    "# Load a blank spaCy model or an existing one\n",
    "nlp = spacy.load('en_core_web_sm')\n",
    "\n",
    "# Add the GLiREL component to the pipeline\n",
    "nlp.add_pipe(\"glirel\", after=\"ner\")\n",
    "\n",
    "# Now you can use the pipeline with the GLiREL component\n",
    "text = \"Apple Inc. was founded by Steve Jobs, Steve Wozniak, and Ronald Wayne in April 1976. The company is headquartered in Cupertino, California.\"\n",
    "\n",
    "labels = {\"glirel_labels\": {\n",
    "    'co-founder': {\"allowed_head\": [\"PERSON\"], \"allowed_tail\": [\"ORG\"]}, \n",
    "    'country of origin': {\"allowed_head\": [\"PERSON\", \"ORG\"], \"allowed_tail\": [\"LOC\", \"GPE\"]}, \n",
    "    'licensed to broadcast to': {\"allowed_head\": [\"ORG\"]},  \n",
    "    'no relation': {},  \n",
    "    'parent': {\"allowed_head\": [\"PERSON\"], \"allowed_tail\": [\"PERSON\"]}, \n",
    "    'followed by': {\"allowed_head\": [\"PERSON\", \"ORG\"], \"allowed_tail\": [\"PERSON\", \"ORG\"]},  \n",
    "    'located in or next to body of water': {\"allowed_head\": [\"LOC\", \"GPE\", \"FAC\"], \"allowed_tail\": [\"LOC\", \"GPE\"]},  \n",
    "    'spouse': {\"allowed_head\": [\"PERSON\"], \"allowed_tail\": [\"PERSON\"]},  \n",
    "    'child': {\"allowed_head\": [\"PERSON\"], \"allowed_tail\": [\"PERSON\"]},  \n",
    "    'founder': {\"allowed_head\": [\"PERSON\"], \"allowed_tail\": [\"ORG\"]},  \n",
    "    'headquartered in': {\"allowed_head\": [\"ORG\"], \"allowed_tail\": [\"LOC\", \"GPE\", \"FAC\"]},  \n",
    "    'acquired by': {\"allowed_head\": [\"ORG\"], \"allowed_tail\": [\"ORG\", \"PERSON\"]},  \n",
    "    'subsidiary of': {\"allowed_head\": [\"ORG\"], \"allowed_tail\": [\"ORG\", \"PERSON\"]}, \n",
    "    }\n",
    "}\n",
    "\n",
    "docs = list( nlp.pipe([(text, labels)], as_tuples=True) )\n",
    "relations = docs[0][0]._.relations\n",
    "\n",
    "print('Number of relations:', len(relations))\n",
    "\n",
    "sorted_data_desc = sorted(relations, key=lambda x: x['score'], reverse=True)\n",
    "print(\"\\nDescending Order by Score:\")\n",
    "for item in sorted_data_desc:\n",
    "    print(f\"{item['head_text']} --> {item['label']} --> {item['tail_text']} | score: {item['score']}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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